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Intelligent non-invasive elderly fall monitoring by designing software defined radio frequency sensing system

Digital Communications and Networks 2024年

作者： Adeel Akram Muhammad Bilal Khan Najah Abed Abu Ali Qixing Zhang Awais Ahmad Muhammad Shahid Iqbal Syed Atif Moqurrab Laboratory for Intelligent Sensing and Computing Institute of Advanced Technology University of Science and Technology of China Hefei 230031 Anhui China State Key Laboratory of Fire Science University of Science and Technology of China Hefei 230026 Anhui China College of IT United Arab Emirates University Al-Ain 15551 United Arab Emirates Department of Electrical and Computer Engineering COMSATS University Islamabad Attock 43600 Pakistan College of Computer and Information Sciences Imam Muhammad Ibn Saud Islamic University (IMSIU) Riyadh 11432 Kingdom of Saudi Arabia Department of Computer Science and Information Technology Women University of AJ&K Bagh Pakistan School of Computing Gachon University 1342 Seongnam-daero Sujeong-gu Seongnam-si 13120 Korea

The global increase in life expectancy poses challenges related to the safety and well-being of the elderly population, especially in relation to falls. While falls can lead to significant cognitive impairments, timely intervention can mitigate their adverse effects. In this context, the need for non-invasive, efficient monitoring systems becomes paramount. Although wearable sensors have gained traction for monitoring health activities, they may cause discomfort during prolonged use, especially for the elderly. To address this issue, we present an intelligent, non-invasive Software-Defined Radio Frequency (SDRF) sensing system, tailored red for monitoring elderly people's falls during routine activities. Harnessing the power of deep learning and machine learning, our system processes the Wireless Channel state Information (WCSI) generated during regular and fall activities. By employing sophisticated signal processing techniques, the system captures unique patterns that distinguish falls from normal activities. In addition, we use statistical features to streamline data processing, thereby optimizing the computational efficiency of the system. Our experiments, conducted for a typical home environment while using treadmill, demonstrate the robustness of the system. The results show high classification accuracies of 92.5%, 95.1%, and 99.8% for three Artificial Intelligence (AI) algorithms. Notably, the SDRF-based approach offers flexibility, cost-effectiveness, and adaptability through software modifications, circumventing the need for hardware overhaul. This research attempts to bridge the gap in RF-based sensing for elderly fall monitoring, providing a solution that combines the benefits of non-invasiveness with the precision of deep learning and machine learning.

关键词： AI Elderly falls Intelligent learning SDRF WCSI

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Time and charge calibration of the LHAASO electromagnetic particle detectors 37

Time and charge calibration of the LHAASO electromagnetic pa...

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37th International Cosmic Ray Conference, ICRC 2021

作者： Pang, Binyu Lv, Hongkui Sheng, Xiangdong Liu, Jia Zhang, Xiaopeng He, Yu Cao, Zhen Aharonian, F. An, Q. Axikegu Bai, L.X. Bai, Y.X. Bao, Y.W. Bastieri, D. Bi, X.J. Bi, Y.J. Cai, H. Cai, J.T. Cao, Zhe Chang, J. Chang, J.F. Chen, B.M. Chen, E.S. Chen, J. Chen, Liang Chen, Liang Chen, Long Chen, M.J. Chen, M.L. Chen, Q.H. Chen, S.H. Chen, S.Z. Chen, T.L. Chen, X.L. Chen, Y. Cheng, N. Cheng, Y.D. Cui, S.W. Cui, X.H. Cui, Y.D. D'Ettorre Piazzoli, B. Dai, B.Z. Dai, H.L. Dai, Z.G. Danzengluobu della Volpe, D. Dong, X.J. Duan, K.K. Fan, J.H. Fan, Y.Z. Fan, Z.X. Fang, J. Fang, K. Feng, C.F. Feng, L. Feng, S.H. Feng, Y.L. Gao, B. Gao, C.D. Gao, L.Q. Gao, Q. Gao, W. Ge, M.M. Geng, L.S. Gong, G.H. Gou, Q.B. Gu, M.H. Guo, F.L. Guo, J.G. Guo, X.L. Guo, Y.Q. Guo, Y.Y. Han, Y.A. He, H.H. He, H.N. He, J.C. He, S.L. He, X.B. Heller, M. Hor, Y.K. Hou, C. Hu, H.B. Hu, S. Hu, S.C. Hu, X.J. Huang, D.H. Huang, Q.L. Huang, W.H. Huang, X.T. Huang, X.Y. Huang, Z.C. Ji, F. Ji, X.L. Jia, H.Y. Jiang, K. Jiang, Z.J. Jin, C. Ke, T. Kuleshov, D. Levochkin, K. Li, B.B. Li, Cheng Li, Cong Li, F. Li, H.B. Li, H.C. Li, H.Y. Li, J. Li, K. Li, W.L. Li, X.R. Li, Xin Li, Xin Li, Y. Li, Y.Z. Li, Zhe Li, Zhuo Liang, E.W. Liang, Y.F. Lin, S.J. Liu, B. Liu, C. Liu, D. Liu, H. Liu, H.D. Liu, J. Liu, J.L. Liu, J.S. Liu, J.Y. Liu, M.Y. Liu, R.Y. Liu, S.M. Liu, W. Liu, Y. Liu, Y.N. Liu, Z.X. Long, W.J. Lu, R. Lv, H.K. Ma, B.Q. Ma, L.L. Ma, X.H. Mao, J.R. Masood, A. Min, Z. Mitthumsiri, W. Montaruli, T. Nan, Y.C. Pang, B.Y. Pattarakijwanich, P. Pei, Z.Y. Qi, M.Y. Qi, Y.Q. Qiao, B.Q. Qin, J.J. Ruffolo, D. Rulev, V. Sáiz, A. Shao, L. Shchegolev, O. Sheng, X.D. Shi, J.Y. Song, H.C. Stenkin, Yu.V. Stepanov, V. Su, Y. Sun, Q.N. Sun, X.N. Sun, Z.B. Tam, P.H.T. Tang, Z.B. Tian, W.W. Key Laboratory of Particle Astrophyics & Experimental Physics Division & Computing Center Institute of High Energy Physics Chinese Academy of Sciences Beijing100049 China University of Chinese Academy of Sciences Beijing100049 China TIANFU Cosmic Ray Research Center Sichuan Chengdu China Dublin Institute for Advanced Studies 31 Fitzwilliam Place Dublin 2 Ireland Max-Planck-Institut for Nuclear Physics P.O. Box 103980 Heidelberg69029 Germany State Key Laboratory of Particle Detection and Electronics China University of Science and Technology of China Anhui Hefei230026 China School of Physical Science and Technology School of Information Science and Technology Southwest Jiaotong University Sichuan Chengdu610031 China College of Physics Sichuan University Sichuan Chengdu610065 China School of Astronomy and Space Science Nanjing University Jiangsu Nanjing210023 China Center for Astrophysics Guangzhou University Guangdong Guangzhou510006 China School of Physics and Technology Wuhan University Hubei Wuhan430072 China Key Laboratory of Dark Matter and Space Astronomy Purple Mountain Observatory Chinese Academy of Sciences Jiangsu Nanjing210023 China Hebei Normal University Hebei Shijiazhuang050024 China Key Laboratory for Research in Galaxies and Cosmology Shanghai Astronomical Observatory Chinese Academy of Sciences Shanghai200030 China Key Laboratory of Cosmic Rays Tibet University Ministry of Education Tibet Lhasa850000 China National Astronomical Observatories Chinese Academy of Sciences Beijing100101 China Sun Yat-sen University Guangdong Zhuhai519000 China Dipartimento di Fisica Università di Napoli "Federico II" Complesso Universitario di Monte Sant'Angelo via Cinthia Napoli80126 Italy School of Physics and Astronomy Yunnan University Yunnan Kunming650091 China D'epartement de Physique Nucl'eaire et Corpusculaire Facult'e de Sciences Universit'e de Gen'eve 24 Quai Ernest Ansermet Geneva1211 Switzerland Institute of Fr

The one square kilometer array (KM2A), a sub-array of Large High Altitude Air Shower Observatory (LHAASO) experiment, consists of 5195 electromagnetic particle detectors (EDs) and 1188 muon detectors (MDs), has been built over three-quarters scale. Its main scientific goal is to search gamma-ray sources at energies above 100 TeV. Offline calibration method has been used to calibrate thousands of EDs to guarantee the key performances of the array such as angular resolution and pointing accuracy within 0.1° during long-term operation. The experimental results of the 3/4 array show that this method can be used to determine the detector time offset with an accuracy of 0.5 ns and the particle number density with an accuracy of a few percent. Furthermore, we monitor the calibration parameters during the array operation and update the calibration results regularly to ensure the data quality of the detector. © 2022 Sissa Medialab Srl. All rights reserved.

关键词： Calibration

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Investigation of thermomechanical one-way and two-way shape memory behaviors of crosslinked poly (ethylene-co-vinyl acetate) / multi-walled carbon nanotube composites

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Smart Materials and Structures 2025年第6期34卷 065022-065022页

作者： Jiawen Shi Naichang Chen Jie Tian Jianping Gu Huiyu Sun Hao Zeng School of Physical and Mathematical Sciences Nanjing Tech University Nanjing 211800 People’s Republic of China Wenzhou Yongliang Mechanical Technology Research Institute Wenzhou 325000 People’s Republic of China Jiangsu Key Laboratory of Advanced Structural Materials and Application Technology School of Materials Science and Engineering Nanjing Institute of Technology Nanjing 211167 People’s Republic of China State Key Laboratory of Mechanics and Control of Mechanical Structures Nanjing University of Aeronautics and Astronautics Nanjing 210016 People’s Republic of China West Taihu Industrial Research College of Nanjing Tech University Changzhou 213100 People’s Republic of China Jinggong Valve Group Co. LTD Wenzhou 325000 People’s Republic of China

This study systematically investigates the thermomechanical characterization of crosslinked poly(ethylene-co-vinyl acetate) (cEVA) nanocomposites reinforced with multi-walled carbon nanotubes (MWCNTs). Through melt-blending and thermo-curing, nanocomposites with 1–5 wt% MWCNTs were synthesized and rigorously evaluated via differential scanning calorimetry, dynamic mechanical analysis, and thermomechanical tests. The nanocomposites exhibit dual-responsive one-way (1 W-SME) and two-way (2 W-SME) shape memory effects under constant stress/strain conditions, driven by crystallization-induced elongation and melting-induced contraction mechanisms, with effective transition temperatures deviating from DSC-measured values due to mechanical loading. Large reversible strains in 2 W-SMEs and good shape fixity/recoverability in 1 W-SMEs were achieved. The study further elucidates the influence of MWCNT content, cooling/heating rates, and external loads on the thermomechanical responses, providing experimental and theoretical foundations for designing programmable actuators and adaptive structures. The integration of nanofiller-mediated crystallization control and thermomechanical coupling mechanisms highlights a novel pathway to tailor high-performance smart materials with predictable recovery and scalability.

关键词：

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The Application of 20 inch MCP-PMT In LHAASO-WCDA 37

The Application of 20 inch MCP-PMT In LHAASO-WCDA

引用

37th International Cosmic Ray Conference, ICRC 2021

作者： You, Xiaohao Gao, Bo Chen, MingJun Li, HuiCai Liu, Cheng Li, Kai Cao, Zhen Aharonian, F. An, Q. Axikegu Bai, L.X. Bai, Y.X. Bao, Y.W. Bastieri, D. Bi, X.J. Bi, Y.J. Cai, H. Cai, J.T. Cao, Zhe Chang, J. Chang, J.F. Chen, B.M. Chen, E.S. Chen, J. Chen, Liang Chen, Liang Chen, Long Chen, M.L. Chen, Q.H. Chen, S.H. Chen, S.Z. Chen, T.L. Chen, X.L. Chen, Y. Cheng, N. Cheng, Y.D. Cui, S.W. Cui, X.H. Cui, Y.D. D'Ettorre Piazzoli, B. Dai, B.Z. Dai, H.L. Dai, Z.G. Danzengluobu della Volpe, D. Dong, X.J. Duan, K.K. Fan, J.H. Fan, Y.Z. Fan, Z.X. Fang, J. Fang, K. Feng, C.F. Feng, L. Feng, S.H. Feng, Y.L. Gao, C.D. Gao, L.Q. Gao, Q. Gao, W. Ge, M.M. Geng, L.S. Gong, G.H. Gou, Q.B. Gu, M.H. Guo, F.L. Guo, J.G. Guo, X.L. Guo, Y.Q. Guo, Y.Y. Han, Y.A. He, H.H. He, H.N. He, J.C. He, S.L. He, X.B. He, Y. Heller, M. Hor, Y.K. Hou, C. Hu, H.B. Hu, S. Hu, S.C. Hu, X.J. Huang, D.H. Huang, Q.L. Huang, W.H. Huang, X.T. Huang, X.Y. Huang, Z.C. Ji, F. Ji, X.L. Jia, H.Y. Jiang, K. Jiang, Z.J. Jin, C. Ke, T. Kuleshov, D. Levochkin, K. Li, B.B. Li, Cheng Li, Cong Li, F. Li, H.B. Li, H.Y. Li, J. Li, W.L. Li, X.R. Li, Xin Li, Xin Li, Y. Li, Y.Z. Li, Zhe Li, Zhuo Liang, E.W. Liang, Y.F. Lin, S.J. Liu, B. Liu, D. Liu, H. Liu, H.D. Liu, J. Liu, J.L. Liu, J.S. Liu, J.Y. Liu, M.Y. Liu, R.Y. Liu, S.M. Liu, W. Liu, Y. Liu, Y.N. Liu, Z.X. Long, W.J. Lu, R. Lv, H.K. Ma, B.Q. Ma, L.L. Ma, X.H. Mao, J.R. Masood, A. Min, Z. Mitthumsiri, W. Montaruli, T. Nan, Y.C. Pang, B.Y. Pattarakijwanich, P. Pei, Z.Y. Qi, M.Y. Qi, Y.Q. Qiao, B.Q. Qin, J.J. Ruffolo, D. Rulev, V. Sáiz, A. Shao, L. Shchegolev, O. Sheng, X.D. Shi, J.Y. Song, H.C. Stenkin, Yu.V. Stepanov, V. Su, Y. Sun, Q.N. Sun, X.N. Sun, Z.B. Tam, P.H.T. Tang, Z.B. Tian, W.W. Wang, B.D. Wang, C. Wang, H. Wang, H.G. Key Laboratory of Particle Astrophyics Experimental Physics Division Computing Center Institute of High Energy Physics Chinese Academy of Sciences 19B Yuquan Road Shijingshan District Beijing100049 China University of Chinese Academy of Sciences Beijing100049 China TIANFU Cosmic Ray Research Center Sichuan Chengdu China Dublin Institute for Advanced Studies 31 Fitzwilliam Place 2 Dublin Ireland Max-Planck-Institut for Nuclear Physics P.O. Box 103980 Heidelberg69029 Germany State Key Laboratory of Particle Detection and Electronics China University of Science and Technology of China Anhui Hefei230026 China School of Physical Science and Technology School of Information Science and Technology Southwest Jiaotong University Sichuan Chengdu610031 China College of Physics Sichuan University Sichuan Chengdu610065 China School of Astronomy and Space Science Nanjing University Jiangsu Nanjing210023 China Center for Astrophysics Guangzhou University Guangdong Guangzhou510006 China School of Physics and Technology Wuhan University Hubei Wuhan430072 China Key Laboratory of Dark Matter and Space Astronomy Purple Mountain Observatory Chinese Academy of Sciences Jiangsu Nanjing210023 China Hebei Normal University Hebei Shijiazhuang050024 China Key Laboratory for Research in Galaxies and Cosmology Shanghai Astronomical Observatory Chinese Academy of Sciences Shanghai200030 China Key Laboratory of Cosmic Rays Tibet University Ministry of Education Tibet Lhasa850000 China National Astronomical Observatories Chinese Academy of Sciences Beijing100101 China Sun Yat-sen University Guangdong Zhuhai519000 China Dipartimento di Fisica Università di Napoli 'Federico II" Complesso Universitario di Monte Sant'Angelo via Cinthia Napoli80126 Italy School of Physics and Astronomy Yunnan University Yunnan Kunming650091 China Departement de Physique Nucleaire et Corpusculaire Faculte de Sciences Universite de Gen'eve 24 Quai Ernest Ansermet Geneva

In the Large High Altitude Air Shower Observatory (LHAASO), the main physics objective of the Water Cherenkov Detector Array (WCDA) is to survey the gamma-ray sky continuously in the energy range from 100 GeV to PeV. The Water Cherenkov detector array, covering an area of about 78,000 m2 area, is constituted by 3120 detector units divided into 3 separate arrays. In the second and third array are installed 2220 20" PMTs instead of the 8 PMT used in the first 150 × 150m2 array. This type of PMT has large sensitive area, high quantum efficiency (QE), and large peak-to-valley (P/V) ratio for single photoelectron detection. In this work, we will report on the application of 20 inch MCP-PMT at LHAASO-WCDA. © Copyright owned by the author(s) under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND 4.0)

关键词： Gamma rays

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High-Performance Mid-IR to Deep-UV van der Waals Photodetectors Capable of Local Spectroscopy at Room Temperature

arXiv

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arXiv 2022年

作者： Shen, Daozhi Yang, HeeBong Spudat, Christian Patel, Tarun Zhong, Shazhou Chen, Fangchu Yan, Jian Luo, Xuan Cheng, Meixin Sciaini, Germán Sun, Yuping Rhodes, Daniel A. Timusk, Thomas Zhou, Y. Norman Kim, Na Young Tsen, Adam W. Institute for Quantum Computing University of Waterloo WaterlooONN2L 3G1 Canada Department of Chemistry University of Waterloo WaterlooONN2L 3G1 Canada Centre for Advanced Materials Joining Department of Mechanical and Mechatronics Engineering University of Waterloo WaterlooONN2L 3G1 Canada Department of Electrical and Computer Engineering University of Waterloo WaterlooONN2L 3G1 Canada Institute of Electrodynamics Microwave and Circuit Engineering TU Wien Gusshausstrasse 25/354 Vienna1040 Austria Department of Physics and Astronomy University of Waterloo WaterlooONN2L 3G1 Canada Key Laboratory of Materials Physics Institute of Solid State Physics HFIPS Chinese Academy of Sciences Hefei230031 China University of Science and Technology of China Hefei230026 China Waterloo Institute for Nanotechnology University of Waterloo WaterlooONN2L 3G1 Canada Anhui Province Key Laboratory of Condensed Matter Physics at Extreme Conditions High Magnetic Field Laboratory HFIPS Chinese Academy of Sciences Hefei230031 China Collaborative Innovation Center of Advanced Microstructures Nanjing University Nanjing210093 China Department of Materials Science and Engineering University of Wisconsin MadisonWI United States Department of Physics and Astronomy McMaster University HamiltonONL8S 4M1 Canada

The ability to perform broadband optical spectroscopy with sub-diffraction-limit resolution is highly sought-after for a wide range of critical applications. However, sophisticated tip-enhanced techniques are currently required to achieve this goal. We bypass this challenge by demonstrating an extremely broadband photodetector based on a two-dimensional (2D) van der Waals heterostructure that is sensitive to light across over a decade in energy from the mid-infrared (MIR) to deep-ultraviolet (DUV) at room temperature. The devices feature high detectivity (> 109 cm Hz1/2 W-1) together with high bandwidth (2.1 MHz). The active area can be further miniaturized to submicron dimensions, far below the diffraction limit for the longest detectable wavelength of 4.1 µm, enabling such devices for facile measurements of local optical properties on atomic-layer-thickness samples placed in close proximity. This work can lead to the development of low-cost and high-throughput photosensors for hyperspectral imaging at the nanoscale. © 2022, CC BY.

关键词： Van der Waals forces

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Low-rank matrix optimization using polynomial-filtered subspace extraction

arXiv

引用

arXiv 2019年

作者： LI, YONGFENG LIU, HAOYANG WEN, ZAIWEN YUAN, YAXIANG Beijing International Center for Mathematical Research Peking University China Beijing International Center for Mathematical Research Peking University State Key Laboratory of Scientific and Engineering Computing Academy of Mathematics and Systems ScinceChinese Academy of Sciences China

In this paper, we study first-order methods on a large variety of low-rank matrix optimization problems, whose solutions only live in a low dimensional eigenspace. Traditional first-order methods depend on the eigenvalue decomposition at each iteration which takes most of the computation time. In order to reduce the cost, we propose an inexact algorithm framework based on a polynomial subspace extraction. The idea is to use an additional polynomial-filtered iteration to extract an approximated eigenspace, and project the iteration matrix on this subspace, followed by an optimization update. The accuracy of the extracted subspace can be controlled by the degree of the polynomial filters. This kind of subspace extraction also enjoys the warm start property: The subspace of the current iteration is refined from the previous one. Then this framework is instantiated into two algorithms: The polynomial-filtered proximal gradient method and the polynomial-filtered alternating direction method of multipliers. We give a theoretical guarantee to the two algorithms that the polynomial degree is not necessarily very large. They share the same convergence speed as the corresponding original methods if the polynomial degree grows with an order (log k) at the k-th iteration. If the warm-start property is considered, the degree can be reduced to a constant, independent of the iteration k. Preliminary numerical experiments on several low-rank matrix optimization problems show that the polynomial filtered algorithms usually provide multi-fold speedups. Copyright © 2019, The Authors. All rights reserved.

关键词： Extraction

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A topology-preserving three-stage framework for fully-connected coronary artery extraction

arXiv

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arXiv 2025年

作者： Qiu, Yuehui Shan, Dandan Wang, Yining Dong, Pei Wu, Dijia Yang, Xinnian Hong, Qingqi Shen, Dinggang Center for Digital Media Computing School of Film School of Informatics Xiamen University Xiamen361005 China Institute of Artificial Intelligence Xiamen University Xiamen361005 China Peking Union Medical College Hospital Beijing100006 China Shanghai United Imaging Intelligence Co. Ltd. Shanghai200232 China City University of Hong Kong 999077 Hong Kong School of Biomedical Engineering State Key Laboratory of Advanced Medical Materials and Devices ShanghaiTech University Shanghai201210 China Shanghai Clinical Research and Trial Center Shanghai200231 China

Coronary artery extraction is a crucial prerequisite for computer-aided diagnosis of coronary artery disease. Accurately extracting the complete coronary tree remains challenging due to several factors, including presence of thin distal vessels, tortuous topological structures, and insufficient contrast. These issues often result in over-segmentation and under-segmentation in current segmentation methods. To address these challenges, we propose a topology-preserving three-stage framework for fully-connected coronary artery extraction. This framework includes vessel segmentation, centerline reconnection, and missing vessel reconstruction. First, we introduce a new centerline enhanced loss in the segmentation process. Second, for the broken vessel segments, we further propose a regularized walk algorithm to integrate distance, probabilities predicted by a centerline classifier, and directional cosine similarity, for reconnecting the centerlines. Third, we apply implicit neural representation and implicit modeling, to reconstruct the geometric model of the missing vessels. Experimental results show that our proposed framework outperforms existing methods, achieving Dice scores of 88.53% and 85.07%, with Hausdorff Distances (HD) of 1.07mm and 1.63mm on ASOCA and PDSCA datasets, respectively. Code will be available at https://***/YH-Qiu/CorSegRec. Copyright © 2025, The Authors. All rights reserved.

关键词： Image segmentation

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Direct visualization of edge state in even-layer MnBi2Te4 at zero magnetic field

arXiv

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arXiv 2021年

作者： Lin, Weiyan Feng, Yang Wang, Yongchao Zhu, Jinjiang Lian, Zichen Zhang, Huanyu Li, Hao Wu, Yang Liu, Chang Wang, Yihua Zhang, Jinsong Wang, Yayu Chen, Chui-Zhen Zhou, Xiaodong Shen, Jian State Key Laboratory of Surface Physics Institute for Nanoelectronic Devices and Quantum Computing Fudan University Shanghai China Department of Physics Fudan University Shanghai China State Key Laboratory of Low Dimensional Quantum Physics Department of Physics Tsinghua University Beijing China School of Physical Science and Technology Soochow University Suzhou China Institute for Advanced Study Soochow University Suzhou China Beijing Innovation Center for Future Chips Tsinghua University Beijing China School of Materials Science and Engineering Tsinghua University Beijing China Tsinghua-Foxconn Nanotechnology Research Center Department of Physics Tsinghua University Beijing China Department of Mechanical Engineering Tsinghua University Beijing China Beijing Academy of Quantum Information Science Beijing China Shanghai Research Center for Quantum Sciences Shanghai China Frontier Science Center for Quantum Information Beijing China Collaborative Innovation Center of Advanced Microstructures Nanjing China Zhangjiang Fudan International Innovation Center Fudan University Shanghai China Shanghai Qi Zhi Institute Shanghai China

Being the first intrinsic antiferromagnetic (AFM) topological insulator (TI), MnBi2Te4 is argued to be a topological axion state in its even-layer form due to the antiparallel magnetization between the top and bottom layers. Here we combine both transport and scanning microwave impedance microscopy (sMIM) to investigate such axion state in atomically thin MnBi2Te4 with even-layer thickness at zero magnetic field. While transport measurements show a zero Hall plateau signaturing the axion state, sMIM uncovers an unexpected edge state raising questions regarding the nature of the "axion state". Based on our model calculation, we propose that the edge state of even-layer MnBi2Te4 at zero field is derived from gapped helical edge states of the quantum spin Hall effect with time-reversal-symmetry breaking, when a crossover from a three-dimensional TI MnBi2Te4 to a two-dimensional TI occurs. Our finding thus signifies the richness of topological phases in MnB2Te4 that has yet to be fully explored. © 2021, CC BY-NC-SA.

关键词： Quantum theory

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Publisher Correction: Switchable long-distance propagation of chiral magnonic edge states

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Nature materials 2025年第3期24卷 472页

作者： Yuelin Zhang Lei Qiu Jilei Chen Shizhe Wu Hanchen Wang Iftikhar Ahmed Malik Miming Cai Mei Wu Peng Gao Chensong Hua Weichao Yu Jiang Xiao Yong Jiang Haiming Yu Ka Shen Jinxing Zhang School of Physics and Astronomy Beijing Normal University Beijing China. Key Laboratory of Multiscale Spin Physics Ministry of Education Beijing China. Fert Beijing Institute MIIT Key Laboratory of Spintronics School of Integrated Circuit Science and Engineering Beihang University Beijing China. International Quantum Academy Shenzhen China. Institute of Quantum Materials and Devices School of Materials Science and Engineering Tiangong University Tianjin China. International Center for Quantum Materials School of Physics Peking University Beijing China. Electron Microscopy Laboratory School of Physics Peking University Beijing China. Collaborative Innovation Center of Quantum Matter Beijing China. Department of Physics and State Key Laboratory of Surface Physics Fudan University Shanghai China. Institute for Nanoelectronic Devices and Quantum Computing Fudan University Shanghai China. Fert Beijing Institute MIIT Key Laboratory of Spintronics School of Integrated Circuit Science and Engineering Beihang University Beijing China. haiming.yu@***. International Quantum Academy Shenzhen China. haiming.yu@***. School of Physics and Astronomy Beijing Normal University Beijing China. kashen@***. Key Laboratory of Multiscale Spin Physics Ministry of Education Beijing China. kashen@***. The Center for Advanced Quantum Studies Beijing Normal University Beijing China. kashen@***. School of Physics and Astronomy Beijing Normal University Beijing China. jxzhang@***. Key Laboratory of Multiscale Spin Physics Ministry of Education Beijing China. jxzhang@***.

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A New Multitasking Malware Classification Model Based on Feature Fusion 2

A New Multitasking Malware Classification Model Based on Fea...

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2nd IEEE advanced Information Management, Communicates, Electronic and Automation Control Conference, IMCEC 2018

作者： Shi, Wei Zhou, Xin Pang, Jianmin Liang, Guanghui Gu, Haoran State Key Laboratory of Mathematics Engineering and Advanced Computing Zhengzhou China Beijing Space Information Relay Transmission Technology Research Center Beijing China

ISBN: (纸本)9781538618035

In this paper, In this paper, we achieved a new Multitask Malware Classification Model Based on DNN (Deep Neural Network), we use the API functions extracted from the dynamic analysis as the main features, the static extracted n-gram, entropy, SYM, Metadata, asm-image and grayscale image as the fusion features to construct a classifier that can deal with the dichotomy problem and the family classification problem. The system was trained in 12 malware families, 3,792 malicious samples and 138 benign samples. The accuracy of the dichotomous problem is 99.61%. The proposed malware family classification model also achieves 99.33% malware family classification error rate. © 2018 IEEE.

关键词： Deep neural networks

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